class_docstr: Implements the log-odds-ratio with an uninformative dirichlet prior from
          Monroe, B. L., Colaresi, M. P., & Quinn, K. M. (2008). Fightin' words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis, 16(4), 372–403.
docstr: Parameters
  ----------
  y_i, np.array(int)
          Arrays of word counts of words occurring in positive class
  y_j, np.array(int)
  
  Returns
  -------
  np.array of z-scores
docstr_tok: ["Parameters","--","--","--","--","--","y_i",",","np.array","(","int",")","Arrays","of","word","counts","of","words","occurring","in","positive","class","y_j",",","np.array","(","int",")","Returns","--","--","--","-","np.array","of","z-scores"]
code: def get_zeta_i_j_given_separate_counts(self, y_i, y_j):
      """
      Parameters
      ----------
      y_i, np.array(int)
              Arrays of word counts of words occurring in positive class
      y_j, np.array(int)
  
      Returns
      -------
      np.array of z-scores
      """
      n_i, n_j = y_i.sum(), y_j.sum()
      prior_scale_j = prior_scale_i = 1
      if self._scale_type == "class-size":
          prior_scale_i = (
              (n_i) * self._scale * 1.0 / np.sum(self._priors)
          )
          prior_scale_j = (
              (n_j) * self._scale * 1.0 / np.sum(self._priors)
          )
      elif self._scale_type == "corpus-size":
          prior_scale_j = prior_scale_i = (
              (n_i + n_j) * self._scale * 1.0 / np.sum(self._priors)
          )
      elif self._scale_type == "word":
          prior_scale_j = prior_scale_i = self._scale / np.sum(
              self._priors
          )
      elif self._scale_type == "background-corpus-size":
          prior_scale_j = prior_scale_i = self._scale
      a_wj = (self._priors * prior_scale_j) ** self._prior_power
      a_0j = np.sum(a_wj)
      a_wi = (self._priors * prior_scale_i) ** self._prior_power
      a_0i = np.sum(a_wi)
  
      delta_i_j = np.log(
          (y_i + a_wi) / (n_i + a_0i - y_i - a_wi)
      ) - np.log((y_j + a_wj) / (n_j + a_0j - y_j - a_wj))
      var_delta_i_j = (
          1.0 / (y_i + a_wi)
          + 1.0 / (n_i + a_0i - y_i - a_wi)
          + 1.0 / (y_j + a_wj)
          + 1.0 / (n_j + a_0j - y_j - a_wj)
      )
      zeta_i_j = delta_i_j / np.sqrt(var_delta_i_j)
      return zeta_i_j
  
file_path: scattertext/scattertext/termsignificance/LogOddsRatioInformativeDirichletPiror.py
imports: import numpy
  from scipy.special import ndtr
  from scattertext.termsignificance import LogOddsRatioUninformativeDirichletPrior
label_desc: Use image node_id 4 for calling the LogOddsRatioInformativeDirichletPrior obj's underlying member method code with example usage: obj.get_zeta_i_j_given_separate_counts(y_i, y_j) and returns: zeta_i_j